Reminders for knitting commands in code chunks - can delete when we’re done

1. Introduction

1.1 Context

Waste diversion comes with global benefits including a decrease in
global warming potential (GWP). However, waste diversion stands to especially benefit those populations living closest to landfills by decreasing local green house gas emissions and other hazards such as odor, smoke, noise, and
water contamination.This project aims to identify which populations live closest to construction and demolition landfills in North Carolina and therefore which populations stand to benefit the most from waste diversion in the construction industry. The focus on the construction industry was inspired by one of the group member’s master’s project which is focused on circularity and waste diversion in the construction industry.

1.2 Research Question

Are construction and demolition landfills in North Carolina disproportionately
more common in low income counties and/or counties with high minority
populations?

1.3 Rational for Data

what’s the difference between this and section 2?

2 Dataset Information

2.1 Source and Content of Data

########does this also count as table with dataset structure? Meghan askin John on 4/1

Dataset Names Relevant Information Source
Site Waste Facility Lists all landfills in NC and includes landfill type and address. NC DEQ https://deq.nc.gov/about/divisions/waste-management/solid-waste-section/solid-waste-facility-lists-presentations-and-annual-reports/solid-waste-facility-lists
CDC Social Vulnerability Contains estimates of poverty and minority data by county. Agency for Toxic Substances and Disease Registry https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2018.html
USA Counties Shape File Spatial data frame that will establish the map of NC. US Census https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html

2.2 Wrangling Process

SVI Variable Name Variable Description
E_TOTPOP Population estimate,2014-2018 ACS
E_POV Persons below poverty estimate,2014-2018 ACS
E_MINRTY Minority (all persons except white, non-Hispanic)estimate,2014-2018 ACS 
#import SVI tract-level data and change FIPS from character to factor 
svi2018_nc_tract_st <-st_read('./Data/Spatial/NorthCarolinaTract/SVI2018_NORTHCAROLINA_tract.shp')
## Reading layer `SVI2018_NORTHCAROLINA_tract' from data source 
##   `Z:\ENV872\ENV872-SeylerHyyppa_Final\Data\Spatial\NorthCarolinaTract\SVI2018_NORTHCAROLINA_tract.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 2192 features and 126 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -84.32187 ymin: 33.84232 xmax: -75.46062 ymax: 36.58812
## Geodetic CRS:  NAD83
svi2018_nc_tract_st$FIPS<-as.factor(svi2018_nc_tract_st$FIPS)
  
mapview(svi2018_nc_tract_st)
#import and select columns of interest from SVI county data and change FIPS from character to factor
svi2018_nc_raw <- read.csv(
  'https://svi.cdc.gov/Documents/Data/2018_SVI_Data/CSV/States_Counties/NorthCarolina_COUNTY.csv',
  colClasses = c('FIPS' = 'factor')) %>% 
  select(COUNTY, FIPS, LOCATION, E_TOTPOP, E_POV, E_MINRTY)

3 Exploratory Analysis

(Rubric: flow between text and visualization is cohesive & relevant exploratory information is visualized)

#check structure
str(svi2018_nc_raw)
## 'data.frame':    100 obs. of  6 variables:
##  $ COUNTY  : chr  "Currituck" "Dare" "Haywood" "Clay" ...
##  $ FIPS    : Factor w/ 100 levels "37001","37003",..: 27 28 44 22 90 88 15 55 85 57 ...
##  $ LOCATION: chr  "Currituck County, North Carolina" "Dare County, North Carolina" "Haywood County, North Carolina" "Clay County, North Carolina" ...
##  $ E_TOTPOP: int  25796 35741 60433 10813 226694 33513 10447 81441 45905 34410 ...
##  $ E_POV   : int  2562 2929 8304 1700 19921 4776 1172 11593 6166 5679 ...
##  $ E_MINRTY: int  3304 4447 4300 680 62132 3249 2047 12168 4022 3684 ...
#check colnames
colnames(svi2018_nc_raw)
## [1] "COUNTY"   "FIPS"     "LOCATION" "E_TOTPOP" "E_POV"    "E_MINRTY"
#investigate what location column includes
glimpse(svi2018_nc_raw$LOCATION)
##  chr [1:100] "Currituck County, North Carolina" ...
#create percent poverty and minority values by county to explore which county's have the highest percentage minority and poverty populations 
povdividedbytotal_col<-(svi2018_nc_raw$E_POV/svi2018_nc_raw$E_TOTPOP)*100

minrtydividedbytotal_col<-(svi2018_nc_raw$E_MINRTY/svi2018_nc_raw$E_TOTPOP)*100

#add the percentage columns to the raw data
svi2018_nc_processed<-svi2018_nc_raw%>%
  add_column(Percent_POV = povdividedbytotal_col)%>%
  add_column(Percent_MINRTY = minrtydividedbytotal_col)

svi2018_nc_poverty_top10 <- svi2018_nc_processed %>%
    arrange(desc(Percent_POV))%>%
    slice(1:10)%>%
    relocate(Percent_POV, .before = FIPS)

svi2018_nc_minrty_top10 <- svi2018_nc_processed %>%
    arrange(desc(Percent_MINRTY))%>%
    slice(1:10)%>%
   relocate(Percent_MINRTY, .before = FIPS)

#create percent poverty and minority values by county to explore which tracts have the highest minority and poverty populations by percentage 

povdividedbytotal_tract_col<-(svi2018_nc_tract_st$E_POV/svi2018_nc_tract_st$E_TOTPOP)*100

minrtydividedbytotal_tract_col<-(svi2018_nc_tract_st$E_MINRTY/svi2018_nc_tract_st$E_TOTPOP)*100

svi2018_nc_tract_st<-svi2018_nc_tract_st%>%
  add_column(Percent_POV = povdividedbytotal_tract_col)%>%
  add_column(Percent_MINRTY = minrtydividedbytotal_tract_col)

svi2018_nc_tract_st<-svi2018_nc_tract_st%>%
  select(COUNTY, FIPS, LOCATION, E_TOTPOP, E_POV, E_MINRTY,Percent_POV,Percent_MINRTY)

svi2018_nc_tract_st_top10 <- svi2018_nc_tract_st %>%
    arrange(desc(Percent_POV))%>%
    slice(1:10)%>%
    relocate(Percent_POV, .before = FIPS)

svi2018_nc_tract_st_top10 <- svi2018_nc_tract_st %>%
    arrange(desc(Percent_MINRTY))%>%
    slice(1:10)%>%
   relocate(Percent_MINRTY, .before = FIPS)
knitr::kable(head(svi2018_nc_poverty_top10[1:10,1:2]), 
             "simple", 
             col.names = c("County","Poverty Percentage"),
             align = 'c',
             caption = "Counties by Highest Poverty Percentage in North Carolina"
)
Counties by Highest Poverty Percentage in North Carolina
County Poverty Percentage
Robeson 27.35571
Jones 26.05467
Scotland 25.60830
Bladen 25.60542
Halifax 24.51630
Alleghany 24.35979 
knitr::kable(head(svi2018_nc_minrty_top10[1:10,1:2]), 
             "simple", 
             col.names = c("County","Minority Percentage"),
             align = 'c',
             caption = "Counties by Highest Percentage of Minority Populations in North Carolina"
)
Counties by Highest Percentage of Minority Populations in North Carolina
County Minority Percentage
Robeson 74.51177
Hertford 66.55488
Bertie 65.71472
Edgecombe 63.63534
Warren 61.68322
Halifax 61.46472 
poverty_plot<-svi2018_nc_processed%>%
  arrange(Percent_POV)%>%
  mutate(COUNTY=factor(COUNTY, COUNTY))%>%
    ggplot(aes(x=COUNTY, y=Percent_POV)) +
      geom_point(color="lightgreen") + 
      geom_segment( aes(x=COUNTY, xend=COUNTY, y=0, yend=Percent_POV))+
      coord_flip()+
      ggtitle("Percentage of Poverty by County in North Carolina") +
      xlab("County Name")+
      ylab("Poverty Population Percentage")+
      theme_minimal()+
      theme(
        axis.text = element_text(size = 5, face='bold') ,
        panel.grid.major.y = element_blank(),
        panel.border = element_blank(),
        axis.ticks.y = element_blank(),
  )

poverty_plot
By Meghan Seyler and Reino Hyyppa

By Meghan Seyler and Reino Hyyppa 

minority_plot<-svi2018_nc_processed%>%
  arrange(Percent_MINRTY)%>%
  mutate(COUNTY=factor(COUNTY, COUNTY))%>%
    ggplot(aes(x=COUNTY, y=Percent_MINRTY)) +
      geom_point(color="69b3a2") + 
      geom_segment( aes(x=COUNTY, xend=COUNTY, y=0, yend=Percent_MINRTY))+
      coord_flip()+
      ggtitle("Percentage of Minority Populations by County in North Carolina") +
      xlab("County Name")+
      ylab("Minority Population Percentage")+
      theme_minimal()+
      theme(
        axis.text = element_text(size = 5, face='bold'),
        panel.grid.major.y = element_blank(),
        panel.border = element_blank(),
        axis.ticks.y = element_blank(),
        plot.subtitle = element_text("By Reino Hyyppa and Meghan Seyler"),
  )
      
minority_plot
By Meghan Seyler and Reino Hyyppa

By Meghan Seyler and Reino Hyyppa